Provides a computationally efficient way of fitting
weighted linear fixed effects estimators for causal
inference with various weighting schemes. Weighted linear
fixed effects estimators can be used to estimate the
average treatment effects under different identification
strategies. This includes stratified randomized
experiments, matching and stratification for
observational studies, first differencing, and
difference-in-differences. The package implements methods
described in Imai and Kim (2017) "When should We Use
Linear Fixed Effects Regression Models for Causal
Inference with Longitudinal Data?", available at
<https://imai.fas.harvard.edu/research/FEmatch.html>.